Package {eq5dsuite}

Type:	Package
Title:	Handling and Analysing EQ-5d Data
Version:	2.0.0
Maintainer:	Kim Rand <krand@mathsinhealth.com>
Description:	The EQ-5D is a widely-used standarized instrument for measuring Health Related Quality Of Life (HRQOL), developed by the EuroQol group https://euroqol.org/. It assesses five dimensions; mobility, self-care, usual activities, pain/discomfort, and anxiety/depression, using either a three-level (EQ-5D-3L) or five-level (EQ-5D-5L) scale. Scores from these dimensions are commonly converted into a single utility index using country-specific value sets, which are critical in clinical and economic evaluations of healthcare and in population health surveys. The eq5dsuite package enables users to calculate utility index values for the EQ-5D instruments, including crosswalk utilities using the original crosswalk developed by van Hout et al. (2012) <doi:10.1016/j.jval.2012.02.008> (mapping EQ-5D-5L responses to EQ-5D-3L index values), or the recently developed reverse crosswalk by van Hout et al. (2021) <doi:10.1016/j.jval.2021.03.009> (mapping EQ-5D-3L responses to EQ-5D-5L index values). Users are allowed to add and/or remove user-defined value sets. Additionally, the package provides tools to analyze EQ-5D data according to the recommended guidelines outlined in "Methods for Analyzing and Reporting EQ-5D data" by Devlin et al. (2020) <doi:10.1007/978-3-030-47622-9>.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	8.0.0
Depends:	R (≥ 3.5)
Imports:	curl, ggplot2, moments, RColorBrewer, rlang, scales, rappdirs
Suggests:	shiny, DT, bslib, readxl, spelling, testthat (≥ 3.0.0), withr, knitr, rmarkdown
VignetteBuilder:	knitr
Config/testthat/edition:	3
Language:	en-US
NeedsCompilation:	no
Packaged:	2026-05-14 08:22:36 UTC; aeste
Author:	Kim Rand [aut, cre], Oliver Rivero-Arias [aut], Iryna Schlackow [aut], Anabel Estévez-Carrillo [aut]
Repository:	CRAN
Date/Publication:	2026-05-14 10:30:02 UTC

eq5dsuite: Standardised Tools for EQ-5D Analysis

Description

The eq5dsuite package provides a comprehensive set of functions for calculating EQ-5D preference-based values and analysing EQ-5D data following the recommendations of Devlin et al. (2020).

The package supports three EQ-5D instruments:

• EQ-5D-3L

• EQ-5D-5L

• EQ-5D-Y-3L

Value calculation

Functions for calculating EQ-5D preference-based values:

• eq5d — Generic calculator for all instruments

• eq5d3l — EQ-5D-3L value calculation

• eq5d5l — EQ-5D-5L value calculation

• eq5dy3l — EQ-5D-Y-3L value calculation

• eqxw — Original crosswalk method (van Hout et al., 2012)

• eqxwr — Reverse crosswalk method (van Hout and Shaw, 2021)

• eqxw_UK — UK-specific NICE-recommended mapping (Hernandez Alava et al., 2023)

Value set management

Functions for managing country-specific value sets:

• eqvs_display — List all available value sets

• eqvs_add — Add a custom value set

• eqvs_drop — Remove a custom value set

• eqvs_load — Load a saved custom value set into the current session

• update_value_sets — Check for and install new value sets from the online repository

Profile analysis

Functions for analysing EQ-5D health state profiles, including cross-sectional summaries, longitudinal change analyses, and severity metrics:

• eq5d_profile_level_summary — Cross-sectional frequency summary by dimension

• eq5d_profile_level_summary_by_group — Stratified frequency summary by subgroup

• eq5d_profile_top_states — Most frequently observed health state profiles

• eq5d_profile_change_summary — Dimension-level frequencies at two time points

• eq5d_profile_dimension_change_table — Changes in levels per dimension as percentages of total and of type of change

• eq5d_profile_pchc_table — Paretian Classification of Health Change table

• eq5d_profile_pchc_with_no_problems_table — PCHC table with no-problems category

• eq5d_profile_pchc_by_group_plot — PCHC bar chart by subgroup

• eq5d_profile_better_dimensions_by_group_plot — Dimensions improved among Better patients

• eq5d_profile_worse_dimensions_by_group_plot — Dimensions worsened among Worse patients

• eq5d_profile_mixed_dimensions_by_group_plot — Dimensions changed among Mixed patients

• eq5d_profile_health_profile_grid — Health Profile Grid of individual transitions

• eq5d_profile_lss_utility_summary — Descriptive statistics by Level Sum Score

• eq5d_profile_lss_utility_plot — EQ-5D values plotted against LSS

• eq5d_profile_lfs_distribution — Level Frequency Score distribution table

• eq5d_profile_lfs_mean_utility — Mean EQ-5D values by LFS category

• eq5d_profile_lfs_utility_summary — Descriptive statistics by Level Frequency Score

• eq5d_profile_lfs_utility_plot — EQ-5D values plotted against LFS

• eq5d_profile_density_curve — Health State Density Curve

EQ-5D value analysis

Functions for analysing EQ-5D preference-based values across time points, subgroups, and population norms:

• eq5d_utility_summary — Descriptive statistics at each time point

• eq5d_utility_summary_by_group — Descriptive statistics stratified by subgroup

• eq5d_utility_norms_comparison — Comparison with population norms by age and sex

• eq5d_utility_over_time_plot — Mean values with confidence intervals over time

• eq5d_utility_by_group_plot — Mean values with confidence intervals by subgroup

• eq5d_utility_change_by_group_plot — Longitudinal change by subgroup

• eq5d_utility_distribution_plot — Histogram of EQ-5D value distribution

• eq5d_utility_vas_scatter_plot — Scatter plot of EQ-5D values against EQ-VAS

EQ-VAS analysis

Functions for analysing EQ-VAS self-rated health scores:

• eq5d_vas_summary — Descriptive statistics at each time point

• eq5d_vas_distribution_table — Frequency distribution in pre-defined ranges

• eq5d_vas_histogram — Histogram of EQ-VAS scores

• eq5d_vas_grouped_distribution_plot — Bar chart of grouped EQ-VAS frequencies

Helper functions

Utility functions for preparing EQ-5D data:

• toEQ5Dindex — Combine dimension columns into a 5-digit profile code

• toEQ5Ddims — Split a 5-digit profile code into separate dimension columns

• make_all_EQ_indexes — Generate all valid EQ-5D state codes as a vector

• make_all_EQ_states — Generate all valid EQ-5D states as a data frame

• make_dummies — Create dummy variables from EQ-5D dimension columns

Interactive application

A Shiny application providing point-and-click access to the same analytical workflow without requiring R coding:

• run_app — Launch the Shiny application

Cross-platform suite

eq5dsuite is part of a cross-platform suite also available in Stata and Excel:

• Full suite: https://github.com/MathsInHealth/eq5dsuite

• R package: https://github.com/MathsInHealth/eq5dsuite-r

• Value sets repository: https://github.com/MathsInHealth/eq5dsuite-value-sets

Author(s)

Anabel Estévez-Carrillo, Oliver Rivero-Arias, Iryna Schlackow, Kim Rand

Maintainer: Anabel Estévez-Carrillo aestevez@mathsinhealth.com

References

Devlin N, Parkin D, Janssen B (2020). Methods for Analysing and Reporting EQ-5D Data. Springer, Cham. doi:10.1007/978-3-030-47622-9

van Hout B, Janssen MF, Feng YS, et al. (2012). Interim scoring for the EQ-5D-5L: mapping the EQ-5D-5L to EQ-5D-3L value sets. Value in Health, 15(5), 708–715. doi:10.1016/j.jval.2012.02.008

.EQxwrprob

Description

Takes a matrix of parameters for reverse crosswalk model, returns 243 x 25 matrix of state/level transition probabilities.

Usage

.EQxwrprob(par = NULL)

Arguments

Value

An 243 * 25 matrix with probabilities for state level transitions.

Add utility values to a data frame

Description

This function adds utility values to a data frame based on a specified version of EQ-5D and a country name.

Usage

.add_utility(df, eq5d_version, country)

Arguments

Value

A data frame with an additional column named 'utility' containing the calculated utility values. If the input country name is not found in the country_codes dataset, a list of available codes is printed, and subsequentyl an error message is displayed and the function stops.

Examples

df <- data.frame(state = c("11111", "11123", "32541"))
.add_utility(df, "5L", "DK")

Check the uniqueness of groups This function takes a data frame 'df' and a vector of columns 'group_by', and checks whether the combinations of values in the columns specified by 'group_by' are unique. If the combinations are not unique, a warning message is printed.

Description

Check the uniqueness of groups This function takes a data frame 'df' and a vector of columns 'group_by', and checks whether the combinations of values in the columns specified by 'group_by' are unique. If the combinations are not unique, a warning message is printed.

Usage

.check_uniqueness(df, group_by)

Arguments

Value

No return value, called for side effects: it will stop with an error if any group combinations are not unique.

Examples

df <- data.frame(id = c(1, 1, 1, 1, 2, 2),
                 fu = rep(c("baseline", "follow-up"), 3),
                 value = rnorm(6))
.check_uniqueness(df, c("id", "fu"))

Helper function for frequency of levels by dimensions tables

Description

Helper function for frequency of levels by dimensions tables

Usage

.freqtab(
  df,
  names_eq5d = NULL,
  name_fu = NULL,
  levels_fu = NULL,
  eq5d_version = NULL,
  add_summary_problems_change = TRUE
)

Arguments

Value

Summary data frame.

Generate colours for PCHC figures

Description

This internal function generates a vector of colours based on the specified base colour. Currently only green and orange colours are implemented. The wrapper is used in Figures 2.2-2.4.

Usage

.gen_colours(col, n)

Arguments

Value

A vector of colours generated based on the specified base colour and number of colours.

Examples

# generate 10 colours for base colour "green"
.gen_colours("green", 10)
# generate 7 colours for base colour "orange"
.gen_colours("orange", 7)

Calculate the Level Frequency Score (LFS)

Description

This function calculates the Level Frequency Score (LFS) for a given EQ-5D state and a specified version of EQ-5D. If at least one domain contains a missing entry, the whole LFS is set to be NA.

Usage

.get_lfs(s, eq5d_version)

Arguments

Value

A character vector representing the calculated LFS.

Examples

.get_lfs("333", "3L") # returns 003
.get_lfs("333", "5L") # returns 00300
.get_lfs("12345", "5L") # returns 11111

Replace NULL names with default values

Description

This function takes in a list of parameters, which would be column names of the input data frame, and checks if they are null. Any nulls are replaced with default values, and the updated list of parameters is returned.

Usage

.get_names(df = NULL, ...)

Arguments

Value

a list of parameters with null entries replaced with default values.

Examples

.get_names(names_eq5d = c("mo", "sc", "ua", "pd", "ad"))
.get_names(names_eq5d = NULL, eq5d_version = NULL, name_vas = NULL)

Get the mode of a vector.

Description

This function calculates the mode of a numeric or character vector. If there are multiple modes, the first one is returned. The code is taken from an R help page.

Usage

.getmode(v)

Arguments

Value

The mode of 'v'.

Examples

.getmode(c(1, 2, 3, 3))
.getmode(c("a", "b", "b", "c"))

Modify ggplot2 theme

Description

Modify ggplot2 theme

Usage

.modify_ggplot_theme(p)

Arguments

Value

ggplot2 plot with modified theme

Wrapper to determine Paretian Classification of Health Change

Description

This internal function determines Paretian Classification of Health Change (PCHC) for each combination of the variables specified in the 'group_by' argument. It is used in the code for eq5d_profile_pchc_table, eq5d_profile_pchc_with_no_problems_table, eq5d_profile_dimension_change_table, and the eq5d_profile_*_by_group_plot functions. An EQ-5D health state is deemed to be 'better' than another if it is better on at least one dimension and is no worse on any other dimension. An EQ-5D health state is deemed to be 'worse' than another if it is worse in at least one dimension and is no better in any other dimension.

Usage

.pchc(df, level_fu_1, add_noprobs = FALSE)

Arguments

Value

A data frame with PCHC value for each combination of the grouping variables. If 'add_noprobs' is TRUE, a separate classification for those without problems is also included.

Examples

df <- data.frame(id = c(1, 1, 2, 2),
                 fu = c(1, 2, 1, 2),
                 mo = c(1, 1, 1, 1),
                 sc = c(1, 1, 5, 1),
                 ua = c(1, 1, 4, 3),
                 pd = c(1, 1, 1, 3),
                 ad = c(1, 1, 1, 1))
.pchc(df, level_fu_1 = 1, add_noprobs = TRUE)

Wrapper to generate Paretian Classification of Health Change plot by dimension

Description

This internal function plots Paretian Classification of Health Change (PCHC) by dimension. The input is a data frame containing the information to plot, and the plot will contain bars representing the proportion of the total data that falls into each dimension, stacked by covariate. The wrapper is used in Figures 2.2-2.4.

Usage

.pchc_plot_by_dim(plot_data, ylab, title, cols, text_rotate = FALSE)

Arguments

Value

A ggplot object containing the PCHC plot.

Examples

df <- data.frame(
  name = rep(c("Dim1", "Dim2"), each = 2),
  p = c(0.6, 0.4, 0.7, 0.3),
  groupvar = rep(c("Group A", "Group B"), 2)
)
colors <- c("Group A" = "#1b9e77", "Group B" = "#d95f02")
.pchc_plot_by_dim(df, ylab = "Proportion", title = "Example Plot", cols = colors)

.pchctab: Changes in health according to the PCHC (Paretian Classification of Health Change)

Description

.pchctab: Changes in health according to the PCHC (Paretian Classification of Health Change)

Usage

.pchctab(
  df,
  name_id,
  name_groupvar,
  names_eq5d = NULL,
  name_fu = NULL,
  levels_fu = NULL,
  add_noprobs = FALSE
)

Arguments

Value

Summary data frame

Examples

.pchctab(df = example_data,
  name_id = "id",
  name_groupvar = "procedure",
  name_fu = "time",
  levels_fu = c('Pre-op', 'Post-op')
)

Data checking/preparation: EQ-5D variables

Description

This function prepares a data frame for analysis by extracting, processing, and adding columns for EQ-5D variables, including state, LSS (Level Sum Score), LFS (Level Frequency Score) and utility.

Usage

.prep_eq5d(
  df,
  names,
  add_state = FALSE,
  add_lss = FALSE,
  add_lfs = FALSE,
  add_utility = FALSE,
  eq5d_version = NULL,
  country = NULL
)

Arguments

Value

a modified data frame with EQ-5D domain columns renamed to default names, and, if necessary, with added columns for state, LSS, LFS, and/or utility. If any of the checks fail (e.g. EQ-5D columns are not in an integer format), an error message is displayed and the function is stopping.

Examples

set.seed(1234)
df <- data.frame(mo = sample(1:5, 3), sc = sample(1:5, 3), 
  ua = sample(1:5, 3), pd = sample(1:5, 3), ad = sample(1:5, 3))
.prep_eq5d(df, names = c("mo", "sc", "ua", "pd", "ad"), 
  add_state = TRUE, add_lss = TRUE)
.prep_eq5d(df, names = c("mo", "sc", "ua", "pd", "ad"),
  add_state = TRUE, add_lss = TRUE, add_lfs = TRUE, add_utility = TRUE,
  eq5d_version = "5L", country = "ES")

Data checking/preparation: follow-up variable

Description

This function prepares the follow-up (FU) variable for analysis by giving it a default name ('fu') and factorising

Usage

.prep_fu(df, name = NULL, levels = NULL)

Arguments

Value

A data frame with the follow-up variable renamed as "fu" and factorised.

Examples

df <- data.frame(id = c(1, 1, 2, 2),
  visit = c("baseline", "follow-up", "baseline", "follow-up"))
.prep_fu(df = df, name = "visit", levels = c("baseline", "follow-up"))

Data checking/preparation: VAS variable

Description

The function prepares the data for VAS (Visual Analogue Scale) analyses.

Usage

.prep_vas(df, name)

Arguments

Value

A modified data frame with the VAS score renamed to "vas". If any checks fail (e.g. column is not numeric), an error message is displayed and the function is stopping.

Examples

df <- data.frame(vas_score = c(20, 50, 80, NA, 100))
.prep_vas(df = df, name = "vas_score")
df <- data.frame(vas_score = c(20.5, 50, 80, NA, 100))
.prep_vas(df = df, name = "vas_score")

.pstate3t5

Description

Takes a N x 25 matrix with probabilities per level/dimension, and creates an N * 3125 matrix with probabilities per state

Usage

.pstate3t5(PPP)

Arguments

Value

An N * 3125 matrix with probabilities per state

.pstate5t3

Description

Takes a 15 x 5 matrix with probabilities per level/dimension, and creates an 3125x243 matrix with probabilities per state

Usage

.pstate5t3(probs = .EQxwprob)

Arguments

Value

An 3125x243 matrix with probabilities per state

Wrapper to summarise a continuous variable by follow-up (FU)

Description

This function summarizes a continuous variable for each follow-up (FU) and calculates various statistics such as mean, standard deviation, median, mode, kurtosis, skewness, minimum, maximum, range, and number of observations. It also reports the total sample size and the number (and proportion) of missing values for each FU. The input 'df' must contain an ordered FU variable and the continuous variable of interest. The name of the continuous variable must be specified using 'name_v'. The wrapper is used in Table 3.1 (for VAS) or Table 4.2 (for EQ-5D utility)

Usage

.summary_cts_by_fu(df, name_v)

Arguments

Value

Data frame with one row for each statistic and one column for each FU.

Examples

df <- data.frame(fu = c(1,1,2,2,3,3), 
                 vas = c(7,8,9,NA,7,6))
.summary_cts_by_fu(df, name_v = "vas")

Wrapper to calculate summary mean with 95% confidence interval

Description

This internal function calculates summary mean and 95% confidence interval of the utility variable, which can also be grouped. The function is used in Figures 4.2-4.4.

Usage

.summary_mean_ci(df, group_by)

Arguments

Value

A data frame with the mean, lower bound, and upper bound of the 95

Examples

df <- data.frame(group = c("A", "A", "B", "B"), 
                 utility = c(0.5, 0.7, 0.8, 0.9))
.summary_mean_ci(df, group_by = "group")

Wrapper for the repetitive code in function_table_2_1. Data frame summary

Description

This internal function summarises a data frame by grouping it based on the variables specified in the 'group_by' argument and calculates the frequency of each group. The output is used in Table 2.1

Usage

.summary_table_2_1(df, group_by)

Arguments

Value

A summarised data frame with groups defined by 'eq5d' and 'fu' variables, the count of observations in each group, and the frequency of each group.

Examples

set.seed(1234)
df <- data.frame(eq5d = rep(rnorm(5), 2),
                 fu = rep(c(1, 0, 1, 0, 1), 2))
.summary_table_2_1(df, c("eq5d", "fu"))

Summary wrapper for Table 4.3

Description

This internal function creates a summary of the data frame for Table 4.3. It groups the data by the variables specified in 'group_by' and calculates various summary statistics.

Usage

.summary_table_4_3(df, group_by)

Arguments

Value

A data frame with the summary statistics.

Examples

df <- data.frame(group = c("A", "A", "B", "B"), 
                 utility = c(0.5, 0.7, 0.8, 0.9))
.summary_table_4_3(df, group_by = "group")

Summary wrapper for Table 4.4

Description

This internal function creates a summary of the data frame for Table 4.4. It groups the data by the variables specified in 'group_by' and calculates various summary statistics.

Usage

.summary_table_4_4(df, group_by)

Arguments

Value

A data frame with the summary statistics.

Examples

df <- data.frame(group = c("A", "A", "B", "B"), 
                 utility = c(0.5, 0.7, 0.8, 0.9))
.summary_table_4_4(df, group_by = "group")

Apply pending value set migrations

Description

Fetches the migrations index from the repository and applies any renames that have not yet been applied locally. This ensures that value set codes remain consistent when a country publishes a second value set and the original code needs to be disambiguated with a year suffix.

Usage

apply_pending_migrations(ask = TRUE)

Arguments

Details

Migrations are applied before checking for new value sets in update_value_sets(), ensuring the local state is consistent before any new installations.

Value

Invisibly returns a character vector of migration IDs that were applied in this session.

Check for conflicts between user-defined and built-in value sets

Description

After applying migrations or installing new value sets, checks whether any built-in value sets share a VS_code with a migration that has been applied. If conflicts are found, warns the user that the built-in value set still exists and will be removed in the next package release.

Usage

check_builtin_conflicts(version)

Arguments

Value

Invisibly returns a character vector of conflicting VS_codes, or character(0) if none found.

Drop a value set from the installed library

Description

Removes a value set from the installed library using eqvs_drop(). Only user-added value sets can be removed — built-in value sets are protected.

Usage

drop_value_set(vs_code, version, ask = TRUE)

Arguments

Value

Logical. TRUE if successful, FALSE otherwise.

eq5d

Description

Get EQ-5D index values for the -3L, -5L, crosswalk (-3L value set applied to -5L health states), reverse crosswealk (-5L value set applied to -3L health states), and -Y-3L

Usage

eq5d(
  x,
  country = NULL,
  version = "5L",
  dim.names = c("mo", "sc", "ua", "pd", "ad")
)

Arguments

Value

A vector of values or data.frame with one column for each value set requested.

Examples

# US -3L value set
eq5d(c(11111, 12321, 32123, 33333), 'US', '3L') 
# Danish and US -5L value sets applied to -3L descriptives, i.e. reverse crosswalk
eq5d(make_all_EQ_states('3L'), c('DK', 'US'), 'XWR') 
# US -5L value set
eq5d(c(11111, 12321, 32153, 55555), 'US', '5L') 

eq5d3l

Description

Get EQ-5D-3L index values from individual responses to the five dimensions of the EQ-5D-3L.

Usage

eq5d3l(x, country = NULL, dim.names = c("mo", "sc", "ua", "pd", "ad"))

Arguments

Value

A numeric vector of EQ-5D-3L values, or a data.frame with one column for each requested value set.

Examples

# Example 1: utility values from EQ-5D-3L profile codes
eq5d3l(c(11111, 12321, 32123, 33333), country = "US")

# Example 2: request multiple value sets
eq5d3l(make_all_EQ_states("3L"), country = c("DK", "CA"))

# Example 3: use a data.frame with dimension columns
df3l <- data.frame(
  mo = c(1, 2, 3),
  sc = c(1, 2, 2),
  ua = c(1, 3, 1),
  pd = c(2, 2, 3),
  ad = c(1, 1, 2)
)
eq5d3l(df3l, country = "US")

# Example 4: use custom dimension column names
df3l_named <- data.frame(
  mobility = c(1, 2, 3),
  self_care = c(1, 2, 2),
  usual_activities = c(1, 3, 1),
  pain_discomfort = c(2, 2, 3),
  anxiety_depression = c(1, 1, 2)
)
eq5d3l(
  df3l_named,
  country = "US",
  dim.names = c(
    "mobility", "self_care", "usual_activities",
    "pain_discomfort", "anxiety_depression"
  )
)

eq5d5l

Description

Get EQ-5D-5L index values from individual responses to the five dimensions of the EQ-5D-5L.

Usage

eq5d5l(x, country = NULL, dim.names = c("mo", "sc", "ua", "pd", "ad"))

Arguments

Value

A numeric vector of EQ-5D-5L values, or a data.frame with one column for each requested value set.

Examples

# Example 1: utility values from EQ-5D-5L profile codes
eq5d5l(c(11111, 12321, 32423, 55555), country = "IT")

# Example 2: request multiple value sets
eq5d5l(make_all_EQ_states("5L"), country = c("ES", "DE"))

# Example 3: use a data.frame with dimension columns
df5l <- data.frame(
  mo = c(1, 2, 5),
  sc = c(1, 2, 4),
  ua = c(1, 3, 3),
  pd = c(2, 4, 2),
  ad = c(1, 5, 1)
)
eq5d5l(df5l, country = "ES")

# Example 4: use custom dimension column names from a real-world style dataset
df5l_named <- data.frame(
  mobility = c(1, 5, 3),
  self_care = c(2, 4, 2),
  usual_activities = c(3, 3, 1),
  pain_discomfort = c(4, 2, 2),
  anxiety_depression = c(5, 1, 3)
)
eq5d5l(
  df5l_named,
  country = "ES",
  dim.names = c(
    "mobility", "self_care", "usual_activities",
    "pain_discomfort", "anxiety_depression"
  )
)

eq5d_profile_better_dimensions_by_group_plot: Percentage of Respondents Who Improved in Each EQ-5D Dimension, by Group This function calculates how many respondents improved in each dimension between two time points and summarizes the results for each group. The, it prodcuces a dimension-focused chart illustrating improvement percentages by dimension.

Description

eq5d_profile_better_dimensions_by_group_plot: Percentage of Respondents Who Improved in Each EQ-5D Dimension, by Group This function calculates how many respondents improved in each dimension between two time points and summarizes the results for each group. The, it prodcuces a dimension-focused chart illustrating improvement percentages by dimension.

Usage

eq5d_profile_better_dimensions_by_group_plot(
  df,
  name_id,
  name_groupvar = NULL,
  names_eq5d = NULL,
  name_fu = NULL,
  levels_fu = NULL
)

Arguments

Value

A list containing:

Examples

result <- eq5d_profile_better_dimensions_by_group_plot(
  df = example_data,
  name_id = "id",
  name_groupvar = "procedure",
  names_eq5d = c("mo", "sc", "ua", "pd", "ad"),
  name_fu = "time",
  levels_fu = c("Pre-op","Post-op")
)
result$p
result$plot_data

eq5d_profile_change_summary: Frequency of levels by dimensions, by follow-up

Description

eq5d_profile_change_summary: Frequency of levels by dimensions, by follow-up

Usage

eq5d_profile_change_summary(
  df,
  names_eq5d = NULL,
  name_fu = NULL,
  levels_fu = NULL,
  eq5d_version = NULL
)

Arguments

Value

Summary data frame.

Examples

eq5d_profile_change_summary(
  df = example_data,
  names_eq5d = c("mo", "sc", "ua", "pd", "ad"),
  name_fu = "time",
  levels_fu = c("Pre-op" , "Post-op"),
  eq5d_version = "3L"
)

eq5d_profile_density_curve: Generate a Health State Density Curve (HSDC) for EQ-5D Data

Description

This function calculates and plots the Health State Density Curve (HSDC) for a given EQ-5D dataset. It concatenates dimension values to form health state profiles, filters out invalid states based on the specified EQ-5D version, then computes the cumulative distribution of profiles (profiles vs. observations). A diagonal reference line indicates a perfectly even distribution. The function also calculates the Health State Density Index (HSDI), representing how sharply the observed distribution deviates from the diagonal.

Usage

eq5d_profile_density_curve(df, names_eq5d, eq5d_version)

Arguments

Value

A list containing:

Examples

figure <- eq5d_profile_density_curve(
            df = example_data, 
            names_eq5d = c("mo", "sc", "ua", "pd", "ad"), 
            eq5d_version = "3L"
          )
figure$plot_data
figure$p

eq5d_profile_dimension_change_table: Changes in levels in each dimension, percentages of total and of type of change

Description

eq5d_profile_dimension_change_table: Changes in levels in each dimension, percentages of total and of type of change

Usage

eq5d_profile_dimension_change_table(
  df,
  name_id,
  names_eq5d = NULL,
  name_fu = NULL,
  levels_fu = NULL
)

Arguments

Value

Summary data frame

Examples

eq5d_profile_dimension_change_table(
  df = example_data,
  name_id = "id",
  names_eq5d = c("mo", "sc", "ua", "pd", "ad"),
  name_fu = "time",
  levels_fu = c("Pre-op" , "Post-op")
)

eq5d_profile_health_profile_grid: Health Profile Grid (HPG) for Two Time Points

Description

This function creates a Health Profile Grid (HPG) for EQ-5D data, plotting each individual's change in health states (ranked from best to worst) between two time points. A diagonal reference line indicates no change; points above the line reflect improvement, and points below indicate deterioration.

Usage

eq5d_profile_health_profile_grid(
  df,
  names_eq5d,
  name_fu,
  levels_fu = NULL,
  name_id,
  eq5d_version,
  country
)

Arguments

Value

A list with components:

Examples

tmp <- eq5d_profile_health_profile_grid(
           df = example_data, 
           names_eq5d = c("mo", "sc", "ua", "pd", "ad"), 
           name_fu = "time", 
           levels_fu = c("Pre-op", "Post-op"), 
           name_id = "id", 
           eq5d_version = "3L", 
           country = "UK"
       )

eq5d_profile_level_summary: Frequency of levels by dimensions, cross-sectional

Description

eq5d_profile_level_summary: Frequency of levels by dimensions, cross-sectional

Usage

eq5d_profile_level_summary(df, names_eq5d = NULL, eq5d_version = NULL)

Arguments

Value

Summary data frame.

Examples

eq5d_profile_level_summary(
 df = example_data[example_data$time == "Pre-op",],
 names_eq5d = c("mo", "sc", "ua", "pd", "ad"),
 eq5d_version = "3L"
)

eq5d_profile_level_summary_by_group: Frequency of levels by dimensions, separated by category

Description

eq5d_profile_level_summary_by_group: Frequency of levels by dimensions, separated by category

Usage

eq5d_profile_level_summary_by_group(
  df,
  names_eq5d = NULL,
  name_cat = NULL,
  levels_cat = NULL,
  eq5d_version = NULL
)

Arguments

Value

Summary data frame.

Examples

eq5d_profile_level_summary_by_group(
 df = example_data[example_data$time == "Pre-op",],
 names_eq5d = c("mo", "sc", "ua", "pd", "ad"),
 name_cat = "procedure",
 levels_cat = c("Hip Replacement", "Knee Replacement"),
 eq5d_version = "3L"
)

eq5d_profile_lfs_distribution: Distribution of the EQ-5D states by LFS (Level Frequency Score)

Description

eq5d_profile_lfs_distribution: Distribution of the EQ-5D states by LFS (Level Frequency Score)

Usage

eq5d_profile_lfs_distribution(df, names_eq5d = NULL, eq5d_version = NULL)

Arguments

Value

Summary data frame

Examples

eq5d_profile_lfs_distribution(
  example_data, 
  names_eq5d = c("mo", "sc", "ua", "pd", "ad"), 
  eq5d_version = "3L"
)

eq5d_profile_lfs_mean_utility: Number of observations in the LFS (Level Frequency Score) according to the EQ-5D values

Description

eq5d_profile_lfs_mean_utility: Number of observations in the LFS (Level Frequency Score) according to the EQ-5D values

Usage

eq5d_profile_lfs_mean_utility(
  df,
  names_eq5d = NULL,
  eq5d_version = NULL,
  country
)

Arguments

Value

Summary data frame

Examples

eq5d_profile_lfs_mean_utility(
  example_data, 
  names_eq5d = c("mo", "sc", "ua", "pd", "ad"), 
  eq5d_version = "3L",
  country = "UK"
)

eq5d_profile_lfs_utility_plot: EQ-5D values plotted against LFS

Description

eq5d_profile_lfs_utility_plot: EQ-5D values plotted against LFS

Usage

eq5d_profile_lfs_utility_plot(
  df,
  names_eq5d = NULL,
  eq5d_version = NULL,
  country
)

Arguments

Value

Summary plot and data used for plotting

Examples

tmp <- eq5d_profile_lfs_utility_plot(
 example_data, 
 names_eq5d = c("mo", "sc", "ua", "pd", "ad"), 
 eq5d_version = "3L",
 country = "UK"
)
tmp$p
tmp$plot_data

eq5d_profile_lfs_utility_summary: Summary statistics of EQ-5D values by LFS (Level Frequency Score)

Description

eq5d_profile_lfs_utility_summary: Summary statistics of EQ-5D values by LFS (Level Frequency Score)

Usage

eq5d_profile_lfs_utility_summary(
  df,
  names_eq5d = NULL,
  eq5d_version = NULL,
  country
)

Arguments

Value

Summary data frame

Examples

eq5d_profile_lfs_utility_summary(
  example_data, 
  names_eq5d = c("mo", "sc", "ua", "pd", "ad"), 
  eq5d_version = "3L",
  country = "UK"
)

eq5d_profile_lss_utility_plot: EQ-5D values plotted against LSS

Description

eq5d_profile_lss_utility_plot: EQ-5D values plotted against LSS

Usage

eq5d_profile_lss_utility_plot(
  df,
  names_eq5d = NULL,
  eq5d_version = NULL,
  country
)

Arguments

Value

Summary plot and data used for plotting

Examples

df <- data.frame(make_all_EQ_states(version = "5L"))
tmp <- eq5d_profile_lss_utility_plot(
 df, 
 names_eq5d = c("mo", "sc", "ua", "pd", "ad"), 
 eq5d_version = "5L", 
 country = "US"
)
tmp$p
tmp$plot_data

eq5d_profile_lss_utility_summary: Summary statistics for the EQ-5D values by all the different LSSs (Level Sum Scores)

Description

eq5d_profile_lss_utility_summary: Summary statistics for the EQ-5D values by all the different LSSs (Level Sum Scores)

Usage

eq5d_profile_lss_utility_summary(
  df,
  names_eq5d = NULL,
  eq5d_version = NULL,
  country
)

Arguments

Value

Summary data frame

Examples

df <- data.frame(make_all_EQ_states(version = "5L"))
eq5d_profile_lss_utility_summary(
  df, 
  names_eq5d = c("mo", "sc", "ua", "pd", "ad"), 
  eq5d_version = "3L", 
  country = "US"
)

eq5d_profile_mixed_dimensions_by_group_plot: Percentage of Respondents Who Had a Mixed Change Overall, by Dimension Improved or Worsened, Grouped by Procedure (or Other Grouping)

Description

This function focuses on patients classified as having "Mixed change" overall (i.e., some dimensions improved, others worsened). It then examines which dimensions improved vs. worsened for each subject. Results are summarized by a grouping variable (e.g., procedure) and time points. The final output is a table plus a ggplot object.

Usage

eq5d_profile_mixed_dimensions_by_group_plot(
  df,
  name_id,
  name_groupvar = NULL,
  names_eq5d = NULL,
  name_fu = NULL,
  levels_fu = NULL
)

Arguments

Value

A list with two elements:

Examples

result <- eq5d_profile_mixed_dimensions_by_group_plot(
  df = example_data,
  name_id = "id",
  name_groupvar = "procedure",
  names_eq5d = c("mo","sc","ua","pd","ad"),
  name_fu = "time",
  levels_fu = c("Pre-op","Post-op")
)
result$plot_data
result$p

eq5d_profile_pchc_by_group_plot: Paretian Classification of Health Change (PCHC) by Group This function computes PCHC categories between two time points for each subject, stratifies them by a grouping variable, and produces a single bar chart with side-by-side bars showing the distribution of PCHC categories.

Description

eq5d_profile_pchc_by_group_plot: Paretian Classification of Health Change (PCHC) by Group This function computes PCHC categories between two time points for each subject, stratifies them by a grouping variable, and produces a single bar chart with side-by-side bars showing the distribution of PCHC categories.

Usage

eq5d_profile_pchc_by_group_plot(
  df,
  name_id,
  name_groupvar = NULL,
  names_eq5d = NULL,
  name_fu = NULL,
  levels_fu = NULL
)

Arguments

Value

A list with two elements:

Examples

result <- eq5d_profile_pchc_by_group_plot(
  df = example_data,
  name_id = "id",
  name_groupvar = "procedure",
  names_eq5d = c("mo", "sc", "ua", "pd", "ad"),
  name_fu = "time",
  levels_fu = c("Pre-op", "Post-op")
)
result$p        # shows the plot
result$plot_data  # shows the summary table

eq5d_profile_pchc_table: Changes in health according to the PCHC (Paretian Classification of Health Change)

Description

eq5d_profile_pchc_table: Changes in health according to the PCHC (Paretian Classification of Health Change)

Usage

eq5d_profile_pchc_table(
  df,
  name_id,
  name_groupvar = NULL,
  names_eq5d = NULL,
  name_fu = NULL,
  levels_fu = NULL
)

Arguments

Value

Summary data frame

Examples

eq5d_profile_pchc_table(
  df = example_data,
  name_id = "id",
  name_groupvar = "procedure",
  names_eq5d = c("mo", "sc", "ua", "pd", "ad"),
  name_fu = "time",
  levels_fu = c("Pre-op" , "Post-op")
)

eq5d_profile_pchc_with_no_problems_table: Changes in health according to the PCHC, taking account of those with no problems

Description

eq5d_profile_pchc_with_no_problems_table: Changes in health according to the PCHC, taking account of those with no problems

Usage

eq5d_profile_pchc_with_no_problems_table(
  df,
  name_id,
  name_groupvar = NULL,
  names_eq5d = NULL,
  name_fu = NULL,
  levels_fu = NULL
)

Arguments

Value

Summary data frame

Examples

eq5d_profile_pchc_with_no_problems_table(
  df = example_data,
  name_id = "id",
  name_groupvar = "procedure",
  names_eq5d = c("mo", "sc", "ua", "pd", "ad"),
  name_fu = "time",
  levels_fu = c("Pre-op" , "Post-op")
)

eq5d_profile_top_states: Prevalence of the 10 most frequently observed self-reported health states

Description

eq5d_profile_top_states: Prevalence of the 10 most frequently observed self-reported health states

Usage

eq5d_profile_top_states(df, names_eq5d = NULL, eq5d_version = NULL, n = 10)

Arguments

Value

Summary data frame

Examples

eq5d_profile_top_states(
  df = example_data[example_data$time == "Pre-op",],
  names_eq5d = c("mo", "sc", "ua", "pd", "ad"),
  eq5d_version = "3L",
  n = 10
)

This function identifies respondents with a "Worsen" PCHC state (i.e., overall health state got worse between levels_fu[1] and levels_fu[2]), checks dimension-specific changes (e.g., mo_diff < 0), and summarizes by a grouping variable (e.g., procedure) and time points. It returns a data table and a ggplot object.

Description

This function identifies respondents with a "Worsen" PCHC state (i.e., overall health state got worse between levels_fu[1] and levels_fu[2]), checks dimension-specific changes (e.g., mo_diff < 0), and summarizes by a grouping variable (e.g., procedure) and time points. It returns a data table and a ggplot object.

Usage

eq5d_profile_worse_dimensions_by_group_plot(
  df,
  name_id,
  name_groupvar = NULL,
  names_eq5d = NULL,
  name_fu = NULL,
  levels_fu = NULL
)

Arguments

Value

A list containing:

Examples

result <- eq5d_profile_worse_dimensions_by_group_plot(
  df = example_data,
  name_id = "id",
  name_groupvar = "procedure",
  names_eq5d = c("mo", "sc", "ua", "pd", "ad"),
  name_fu = "time",
  levels_fu = c("Pre-op","Post-op")
)
result$p        # shows the plot
result$plot_data  # shows the summary table

eq5d_utility_by_group_plot: Mean EQ-5D values and 95% confidence intervals: all vs by groupvar

Description

eq5d_utility_by_group_plot: Mean EQ-5D values and 95% confidence intervals: all vs by groupvar

Usage

eq5d_utility_by_group_plot(
  df,
  names_eq5d = NULL,
  name_groupvar = NULL,
  eq5d_version = NULL,
  country
)

Arguments

Value

Summary plot and data used for plotting

Examples

tmp <- eq5d_utility_by_group_plot(
 example_data,
 names_eq5d = c("mo", "sc", "ua", "pd", "ad"), 
 name_groupvar = "procedure",
 eq5d_version = "3L",
 country = "UK"
)
tmp$p
tmp$plot_data

eq5d_utility_change_by_group_plot: EQ-5D values: smoothed lines and confidence intervals by groupvar

Description

eq5d_utility_change_by_group_plot: EQ-5D values: smoothed lines and confidence intervals by groupvar

Usage

eq5d_utility_change_by_group_plot(
  df,
  names_eq5d = NULL,
  name_fu = NULL,
  levels_fu = NULL,
  name_groupvar = NULL,
  eq5d_version = NULL,
  country
)

Arguments

Value

Summary plot and data used for plotting

Examples

tmp <- eq5d_utility_change_by_group_plot(
 example_data,
 names_eq5d = c("mo", "sc", "ua", "pd", "ad"), 
 name_fu = "time",
 levels_fu = c('Pre-op', 'Post-op'),
 name_groupvar = "procedure",
 eq5d_version = "3L",
 country = "UK"
)
tmp$p
tmp$plot_data

eq5d_utility_distribution_plot: EQ-5D values: smoothed lines and confidence intervals by groupvar

Description

eq5d_utility_distribution_plot: EQ-5D values: smoothed lines and confidence intervals by groupvar

Usage

eq5d_utility_distribution_plot(
  df,
  names_eq5d = NULL,
  eq5d_version = NULL,
  country
)

Arguments

Value

Summary plot and data used for plotting

Examples

tmp <- eq5d_utility_distribution_plot(
 example_data,
 names_eq5d = c("mo", "sc", "ua", "pd", "ad"), 
 eq5d_version = "3L",
 country = "UK"
)
tmp$p
tmp$plot_data

eq5d_utility_norms_comparison:EQ-5D values: by age and groupvar

Description

eq5d_utility_norms_comparison:EQ-5D values: by age and groupvar

Usage

eq5d_utility_norms_comparison(
  df,
  names_eq5d = NULL,
  name_fu = NULL,
  levels_fu = NULL,
  name_groupvar = NULL,
  name_age,
  eq5d_version = NULL,
  country
)

Arguments

Value

Summary data frame

Examples

example_data$ageband <- factor(
  example_data$ageband,
  levels = c("20 to 29", "30 to 39", "40 to 49", "50 to 59", "60 to 69", "70 to 79", "80 to 89")
)
example_data <- example_data[example_data$gender %in% c("Male", "Female"),]
eq5d_utility_norms_comparison(
  example_data,
  names_eq5d = c("mo", "sc", "ua", "pd", "ad"),
  name_fu = "time",
  levels_fu = c('Pre-op', 'Post-op'),
  name_groupvar = "gender",
  name_age = "ageband",
  eq5d_version = "3L",
  country = "UK"
)

eq5d_utility_over_time_plot: EQ-5D values by timepoints: mean values and 95% confidence intervals

Description

eq5d_utility_over_time_plot: EQ-5D values by timepoints: mean values and 95% confidence intervals

Usage

eq5d_utility_over_time_plot(
  df,
  names_eq5d = NULL,
  name_fu = NULL,
  levels_fu = NULL,
  eq5d_version = NULL,
  country
)

Arguments

Value

Summary plot and data used for plotting

Examples

tmp <- eq5d_utility_over_time_plot(
 example_data,
 names_eq5d = c("mo", "sc", "ua", "pd", "ad"), 
 name_fu = "time",
 levels_fu = c('Pre-op', 'Post-op'),
 eq5d_version = "3L",
 country = "UK"
)
tmp$p
tmp$plot_data

eq5d_utility_summary: EQ-5D values: by timepoints

Description

eq5d_utility_summary: EQ-5D values: by timepoints

Usage

eq5d_utility_summary(
  df,
  names_eq5d = NULL,
  name_fu = NULL,
  levels_fu = NULL,
  eq5d_version = NULL,
  country
)

Arguments

Value

Summary data frame

Examples

eq5d_utility_summary(
  example_data,
  names_eq5d = c("mo", "sc", "ua", "pd", "ad"), 
  name_fu = "time",
  levels_fu = c('Pre-op', 'Post-op'),
  eq5d_version = "3L",
  country = "UK"
)

eq5d_utility_summary_by_group:EQ-5D values: by groupvar

Description

eq5d_utility_summary_by_group:EQ-5D values: by groupvar

Usage

eq5d_utility_summary_by_group(
  df,
  names_eq5d = NULL,
  name_groupvar = NULL,
  eq5d_version = NULL,
  country
)

Arguments

Value

Summary data frame

Examples

eq5d_utility_summary_by_group(
  example_data,
  names_eq5d = c("mo", "sc", "ua", "pd", "ad"),
  name_groupvar = "procedure",
  eq5d_version = "3L",
  country = "UK"
)

eq5d_utility_vas_scatter_plot: EQ-5D values: smoothed lines and confidence intervals by groupvar

Description

eq5d_utility_vas_scatter_plot: EQ-5D values: smoothed lines and confidence intervals by groupvar

Usage

eq5d_utility_vas_scatter_plot(
  df,
  names_eq5d = NULL,
  name_vas = NULL,
  eq5d_version = NULL,
  country
)

Arguments

Value

Summary plot and data used for plotting

Examples

tmp <- eq5d_utility_vas_scatter_plot(
   example_data,
   names_eq5d = c("mo", "sc", "ua", "pd", "ad"),
   name_vas = "vas",
   eq5d_version = "3L",
   country = "UK"
 )
tmp$p
tmp$plot_data

eq5d_vas_distribution_table: EQ VAS Scores frequency of mid-points

Description

eq5d_vas_distribution_table: EQ VAS Scores frequency of mid-points

Usage

eq5d_vas_distribution_table(df, name_vas = NULL, add_na_total = TRUE)

Arguments

Value

Summary data frame

Examples

eq5d_vas_distribution_table(
  example_data,
  name_vas = 'vas', 
  add_na_total =  TRUE
  )

eq5d_vas_grouped_distribution_plot: Mid-point EQ VAS scores

Description

eq5d_vas_grouped_distribution_plot: Mid-point EQ VAS scores

Usage

eq5d_vas_grouped_distribution_plot(df, name_vas = NULL)

Arguments

Value

Summary plot and data used for plotting

Examples

tmp <- eq5d_vas_grouped_distribution_plot(example_data, name_vas = 'vas')
tmp$p
tmp$plot_data

eq5d_vas_histogram: EQ VAS scores

Description

eq5d_vas_histogram: EQ VAS scores

Usage

eq5d_vas_histogram(df, name_vas = NULL)

Arguments

Value

Summary plot and data used for plotting

Examples

tmp <- eq5d_vas_histogram(example_data, name_vas = 'vas')
tmp$p
tmp$plot_data

eq5d_vas_summary: EQ VAS Score by timepoints

Description

eq5d_vas_summary: EQ VAS Score by timepoints

Usage

eq5d_vas_summary(df, name_vas = NULL, name_fu = NULL, levels_fu = NULL)

Arguments

Value

Summary data frame

Examples

eq5d_vas_summary(
  example_data,
  name_vas = 'vas', 
  name_fu = 'time', 
  levels_fu = c('Pre-op', 'Post-op')
)

eq5dy3l

Description

Get EQ-5D-Y-3L index values from individual responses to the five dimensions of the EQ-5D-Y-3L.

Usage

eq5dy3l(x, country = NULL, dim.names = c("mo", "sc", "ua", "pd", "ad"))

Arguments

Value

A numeric vector of EQ-5D-Y-3L values, or a data.frame with one column for each requested value set.

Examples

# Example 1: utility values from EQ-5D-Y-3L profile codes
eq5dy3l(x = c(11111, 12321, 33333), country = "SI")

# Example 2: request multiple value sets
eq5dy3l(make_all_EQ_states("3L"), country = c("ES", "DE"))

# Example 3: use a data.frame with dimension columns
dfy3l <- data.frame(
  mo = c(1, 2, 3),
  sc = c(1, 1, 2),
  ua = c(1, 2, 3),
  pd = c(2, 2, 3),
  ad = c(1, 3, 2)
)
eq5dy3l(dfy3l, country = "SI")

# Example 4: use custom dimension column names
dfy3l_named <- data.frame(
  mobility = c(1, 2, 3),
  self_care = c(1, 1, 2),
  usual_activities = c(1, 2, 3),
  pain_discomfort = c(2, 2, 3),
  anxiety_depression = c(1, 3, 2)
)
eq5dy3l(
  dfy3l_named,
  country = "SI",
  dim.names = c(
    "mobility", "self_care", "usual_activities",
    "pain_discomfort", "anxiety_depression"
  )
)

eqvs_add

Description

Add user-defined EQ-5D value set and corresponding crosswalk option.

Usage

eqvs_add(
  df,
  version = "5L",
  country = NULL,
  countryCode = NULL,
  VSCode = NULL,
  description = NULL,
  saveOption = 1,
  savePath = NULL
)

Arguments

Value

True/False, indicating success or error.

Examples

# make nonsense value set
new_df <- data.frame(state = make_all_EQ_indexes(), TEST = runif(3125))
# Add as value set for Fantasia
eqvs_add(
   new_df,
   version = "5L",
   country = 'Fantasia',
   countryCode = "MyCountry",
   VSCode = "FAN",
   saveOption = 1
)
eq5d5l(55555,country = "FAN")

eqvs_display

Description

Display available value sets, which can also be used as (reverse) crosswalks. Built-in value sets are shown first, followed by any user-defined value sets added via eqvs_add().

Usage

eqvs_display(version = "5L", return_df = FALSE, show_citation = FALSE)

Arguments

Value

NULL invisibly (default), or a data.frame when return_df = TRUE.

Examples

# Display available EQ-5D-5L value sets.
eqvs_display(version = "5L")

eqvs_drop

Description

Drop user-defined EQ-5D value set to reverse crosswalk options.

Usage

eqvs_drop(country = NULL, version = "5L", saveOption = 1, savePath = NULL)

Arguments

Value

True/False, indicating success or error.

Examples

  # make nonsense value set
  new_df <- data.frame(state = make_all_EQ_indexes(), TEST = runif(3125))
  # Add as value set for Fantasia
  eqvs_add(
   new_df,
   version = "5L",
   country = 'Fantasia',
   countryCode = "MyCountry",
   VSCode = "FAN",
   saveOption = 1
  )
  # Test the new value set
  eq5d5l(55555,country = "FAN")
  # Drop value set for Fantasia
  eqvs_drop(country = 'FAN', saveOption = 1)

eqvs_load

Description

Load cache data from a specified path.

Usage

eqvs_load(loadPath)

Arguments

Value

TRUE if loading is successful, FALSE otherwise.

eqxw

Description

Get crosswalk values

Usage

eqxw(x, country = NULL, dim.names = c("mo", "sc", "ua", "pd", "ad"))

Arguments

Value

A vector of reverse crosswalk values or data.frame with one column per reverse crosswalk set requested.

Examples

eqxw(c(11111, 12521, 32123, 55555), 'US')
eqxw(make_all_EQ_states('5L'), c('DK', 'US'))

eqxw_UK

Description

Crosswalks EQ-5D-5L responses to EQ-5D-3L utilities using NICE's mapping.

Usage

eqxw_UK(x, age, male, dim.names = c("mo", "sc", "ua", "pd", "ad"), bwidth = 0)

Arguments

Value

A vector or data frame with crosswalked EQ-5D-3L utilities.

Examples

eqxw_UK(c(11111, 12345, 32423, 55555), age = c(30, 40, 55, 70), male = c(1, 0, 1, 0))

eqxwr

Description

Get reverse crosswalk values

Usage

eqxwr(x, country = NULL, dim.names = c("mo", "sc", "ua", "pd", "ad"))

Arguments

Value

A vector of reverse crosswalk values or data.frame with one column per reverse crosswalk set requested.

Examples

eqxwr(c(11111, 12321, 32123, 33333), 'US')
eqxwr(make_all_EQ_states('3L'), c('DK', 'US'))

example_data

Description

A dataset containing patient-level data in a long format.

Usage

data(example_data)

Format

A data frame with 10000 rows and 14 variables:

id

double Patient id

time

character Follow-up (Pre-op / Post-op)

mo

double EQ-5D-5L Mobility dimension

sc

double EQ-5D-5L Self-care dimension

ua

double EQ-5D-5L Usual activities dimension

pd

double EQ-5D-5L Pain / discomfort dimension

ad

double EQ-5D-5L Anxiety/depression dimension

vas

double Value of the VAS scale measurememnt

providercode

character Provider code

procedure

character Type of surgery

year

character Year of intervention

ageband

character Age in pre-defined ranges

gender

character Patient's gender (Female / Male)

Fetch the index of available value sets from GitHub

Description

Fetch the index of available value sets from GitHub

Usage

fetch_available_value_sets(version)

Arguments

Value

A data frame with columns Version, Name, Name_short, Country_code, VS_code, doi. Returns NULL if fetch fails.

Fetch the migrations index from the repository

Description

Downloads the migrations.csv file from the eq5dsuite-value-sets repository, which records all historical value set renames. Returns NULL silently if the fetch fails.

Usage

fetch_migrations()

Value

A data frame with columns version, old_VS_code, new_VS_code, reason, date. Returns NULL if fetch fails.

Get the list of already-applied migrations

Description

Reads the locally cached list of migration IDs that have already been applied. Returns an empty character vector if no migrations have been applied yet.

Usage

get_applied_migrations()

Value

A character vector of migration IDs.

Get the eq5dsuite cache directory

Description

Returns the path to the user-specific cache directory used by eq5dsuite to store the date of the last value set check. The directory is created if it does not exist.

Usage

get_cache_dir()

Value

A character string giving the path to the cache directory.

Get the VS_codes currently installed for a given instrument

Description

Returns all installed VS_codes for the specified instrument version, including both built-in value sets shipped with the package and any user-added value sets installed via eqvs_add() or update_value_sets().

Usage

get_installed_vs_codes(version)

Arguments

Details

Reads directly from the runtime package environment (getOption("eq.env")) to include user-added value sets without triggering the display side-effects of eqvs_display().

Value

A character vector of VS_codes.

Get the date of the last value set check

Description

Reads the cached date of the last value set update check from the user cache directory. Returns 1970-01-01 if no cache file exists (i.e., the check has never been run).

Usage

get_last_checked()

Value

A Date object.

Download and install a single value set

Description

Download and install a single value set

Usage

install_value_set(vs_code, version, meta_row)

Arguments

Value

Logical. TRUE if successful, FALSE otherwise.

Check whether a value set update check is due

Description

Compares the date of the last value set check (from the user cache) against Sys.Date(). Returns TRUE if more than threshold_days days have elapsed, indicating that the user should be prompted to run update_value_sets().

Usage

is_update_due(threshold_days = 60)

Arguments

Value

A single logical value.

make_all_EQ_indexes

Description

Make a vector containing all 5-digit EQ-5D indexes for -3L or -5L version.

Usage

make_all_EQ_indexes(
  version = "5L",
  dim.names = c("mo", "sc", "ua", "pd", "ad")
)

Arguments

Value

A vector with 5-digit state indexes for all 243 (-3L) or 3125 (-5L) EQ-5D health states

Examples

make_all_EQ_indexes('3L')

make_all_EQ_states

Description

Make a data.frame with all health states defined by dimensions

Usage

make_all_EQ_states(
  version = "5L",
  dim.names = c("mo", "sc", "ua", "pd", "ad"),
  append_index = FALSE
)

Arguments

Value

A data.frame with 5 columns and 243 (-3L) or 3125 (-5L) health states

Examples

make_all_EQ_states('3L')

EQ_dummies

Description

Make a data.frame of all EQ-5D dummies relevant for e.g. regression modeling.

Usage

make_dummies(
  df,
  version = "5L",
  dim.names = c("mo", "sc", "ua", "pd", "ad"),
  drop_level_1 = TRUE,
  add_intercept = FALSE,
  incremental = FALSE,
  prepend = NULL,
  append = NULL,
  return_df = TRUE
)

Arguments

Value

A data.frame of dummy variables

Examples

make_dummies(make_all_EQ_states('3L'), '3L')

make_dummies(df = make_all_EQ_states('3L'), 
             version =  '3L', 
             incremental = TRUE, 
             add_intercept = TRUE, 
             prepend = "d_")

Rename a value set

Description

Renames a value set by dropping the old VS_code and reinstalling under the new VS_code. The value data is preserved — only the identifier changes.

Usage

rename_value_set(old_vs_code, new_vs_code, version, ask = TRUE)

Arguments

Value

Logical. TRUE if successful, FALSE otherwise.

Launch the eq5dsuite Shiny Application

Description

Opens an interactive Shiny application for uploading, processing, analysing, and exporting EQ-5D data using the eq5dsuite package.

Usage

run_app(...)

Arguments

Value

Called for its side effect of launching a Shiny application. Returns invisibly.

Examples

## Not run: 
  eq5dsuite::run_app()

## End(Not run)

Save the list of applied migrations to cache

Description

Save the list of applied migrations to cache

Usage

set_applied_migrations(migrations)

Arguments

Value

Invisibly returns the saved migrations.

Set the date of the last value set check

Description

Writes the given date to the user cache directory so that future calls to get_last_checked() and is_update_due() can determine how long ago the check was performed.

Usage

set_last_checked(date = Sys.Date())

Arguments

Value

Invisibly returns date.

toEQ5Ddims

Description

Generate dimension vectors based on state index

Usage

toEQ5Ddims(x, dim.names = c("mo", "sc", "ua", "pd", "ad"))

Arguments

Value

A data.frame with 5 columns, one for each EQ-5D dimension, with names from dim.names argument.

Examples

toEQ5Ddims(c(12345, 54321, 12321))

Convert EQ-5D dimension scores to a five-digit profile index

Description

Convert EQ-5D dimension scores to a five-digit profile index

Usage

toEQ5Dindex(
  x,
  dim.names = c("mo", "sc", "ua", "pd", "ad"),
  na.rm = FALSE,
  quiet = FALSE
)

Arguments

Value

An integer vector with one element per row (data.frame/matrix input) or a single integer (vector input). Works inside dplyr::mutate() without rowwise().

Examples

# Named vector — scalar usage unchanged
toEQ5Dindex(c(mo=1, sc=2, ua=3, pd=1, ad=2))

Update eq5dsuite value sets

Description

Connects to the eq5dsuite value sets repository and checks whether new value sets are available for any of the three EQ-5D instruments. Optionally drop or rename existing value sets before checking for updates.

Usage

update_value_sets(
  versions = c("3L", "5L", "Y3L"),
  ask = TRUE,
  drop = NULL,
  rename = NULL
)

Arguments

Details

This function requires an internet connection for checking and installing new value sets. Drop and rename operations work offline.

Value

Invisibly returns a list with elements checked, new, installed, dropped, and renamed.

Examples

## Not run: 
# Check for new value sets interactively
update_value_sets()

# Check specific instrument only
update_value_sets(versions = "5L")

# Drop a value set
update_value_sets(drop = "NL:3L")

# Rename a value set
update_value_sets(rename = c("NL:3L" = "NL_2006"))

# Drop and rename in one call
update_value_sets(
  drop   = "DE:3L",
  rename = c("NL:3L" = "NL_2006", "SI:3L" = "SI_TTO")
)

# Non-interactive update
update_value_sets(ask = FALSE)

## End(Not run)